1. Spatial and Temporal Pattern of
Air Pollution over China Based on
Remote Sensing Observations
Rudolf B. Husar
With
Li Du, Erin Robinson
Washington University, St. Louis, MO, USA
Seminar at Fudan University,
May 28, 2010, Shanghai, China

2. Atmospheric Aerosol Challenge:
Characterization of Aerosols
Dimension
Abbr.
Data Sources
Spatial dimensions
X, Y
Satellites, dense networks
Height
Z
Lidar, soundings
Time
T
Continuous monitoring
Particle size
D
Size-segregated sampling
Particle Composition
C
Speciated analysis
Particle Shape/Mixing
S
Microscopy, Source Type
•
•
Aerosol complexity is due 7-dim. data space
The ‘aerosol dimensions’ D, C, S determine the
effects on health and climate
Aerosol concentration:
a (X, Y, Z, T, D, C, S)

3. Challenge: Vertical Distribution of Aerosols
Vertical Distribution:
• Layering
• Size Distr.
• Composition

4. •
Technical Challenge:
Characterization
PM characterization requires many different instruments and analysis tools.
• Each sensor/network covers only a fraction of the 7-Dim PM data space.
Satellite-Integral
Satellites, integrate over height,
size, composition, shape…
dimensions
These data need de-convolution of
the integral measures

5. Satellite Remote Sensing Since 1972
•
•
•
First satellite aerosol paper, Francis Parmenter, 1972
Qualitative surface-satellite aerosol relationship shown, 1976
Focus on regional ‘hazy blobs’, sulfate pollution
Regional Haze
Lyons W.A., Husar R.B. Mon. Weather Rev. 1976
SMS GOES June 30 1975

6. Satellites show the synoptic aerosol pattern
and provide rich spatial context …
e.g. pollution in valleys.
Jan 10, 2003, SeaWiFS
Dec 19, 2007, MODIS

7. The Perfect Dust Storm…
Apr. 7, 2001 SaeWiFS

8. Asian Dust Cloud over N. America
In Washington State, PM10
100 µg/m3
concentrations exceeded 100 µg/m3
Asian Dust
Hourly PM10
On April 27, the dust cloud arrived in
North America.
Regional average PM10
concentrations increased to 65 µg/m3

9. Satellite
(MODIS, OMI)
Sun Photometer
(Aeronet)
Visual Range
(WMO)

10. MISR AOT
Challenge: Aerosol Retrieval Quality
Land
MODIS-AOT
Ocean
MODIS-AOT
MODIS vs. MISR: Poor AOT Correlation over Land

11. Aerosol AOT – MODIS
January
April
July
October

12. AERONET – MODIS AOT Comparison
Beijing
MODIS
Aeronet
MODIS/Aeronet Ratio
Hong Kong
Correlations good but systematic differences (slope)

13. MODIS4 AOT: Thursday

14. MODIS4 AOT: Sunday

15. MODIS4 AOT: Thursday

16. MODIS4 AOT: Sunday

17. MODIS Fire Pixels

18. OMI Absorbing Aerosol Index
January
April
July
October

19. OMI CHCO Formaldehyde
January
April
July
October

20. Population
density
Emissions - NOx

21. Satellite Column Concentration: NO2, 2005
OMI Spectrometer Sensor

22. Satellite Column Concentration: NO2, 2009
OMI Spectrometer Sensor

23. OMI NO2 Day of Week: Thursday

24. OMI NO2 Day of Week: Sunday

25. Visibility is recorded at 7000+ stations hourly

26. Visual Range, Guilin 2010-05-23,24

27. Visual Range 16 km
Visual Range 9 km

28. Guilin

29. Surface Extinction Coefficient
Jun, Jul, Aug
Dec, Jan, Feb

31. Sechuan Basin
Jun, Jul, Aug
Chengdu
Chongqing
Dec, Jan, Feb
Chengdu
Chongqing
MODIS
VISIBILITY

32. Xi’an
Jun, Jul, Aug
Xi’an
Tianjin
Dec, Jan, Feb
Xi’an
Tianjin
MODIS
VISIBILITY

33. Summary
•Each data set has limitations, but gives self-consistent global-scale observations
• More detailed measurements are essential for the understanding
•There are still enormous challenges in integrating multi-sensory data for
characterizing aerosols.
Combining global-scale remote sensing observations with detailed local
observations and research conducted in China could yield faster
progress.

34. International Collaboration Opportunity:
Global Observing System of Systems (GEOSS)
Pooling of Earth Observations nine Societal Benefit Areas
Any Dataset
Serves Many
Communities
Any Problem
Requires Many
Datasets
New International Program - China is a Co-Chair of GEOSS EE